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Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem

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Abazovic, F. (2018). Nvidia stock dropped for the wrong reason. Retrieved from https://www.fudzilla.com/news/ai/47637-nvidia-stock-dropped-for-the-wrong-reason AbazovicF. 2018 Nvidia stock dropped for the wrong reason Retrieved from https://www.fudzilla.com/news/ai/47637-nvidia-stock-dropped-for-the-wrong-reason Search in Google Scholar

Abe, M., & Nakayama, H. (2018). Deep learning for forecasting stock returns in the cross-section. Pacific-Asia Conference on Knowledge Discovery and Data Mining. 273–284. https://doi.org/10.1007/978-3-319-93034-3_22 AbeM. NakayamaH. 2018 Deep learning for forecasting stock returns in the cross-section Pacific-Asia Conference on Knowledge Discovery and Data Mining 273 284 https://doi.org/10.1007/978-3-319-93034-3_22 10.1007/978-3-319-93034-3_22 Search in Google Scholar

Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics 2014. https://doi.org/10.1155/2014/614342 AdebiyiA. A. AdewumiA. O. AyoC. K. 2014 Comparison of ARIMA and artificial neural networks models for stock price prediction Journal of Applied Mathematics 2014 https://doi.org/10.1155/2014/614342 10.1155/2014/614342 Search in Google Scholar

Adhikari, R., Verma, G., & Khandelwal, I. (2015). A model ranking based selective ensemble approach for time series forecasting. Procedia Computer Science, 48, 14–21. https://doi.org/10.1016/j.procs.2015.04.104 AdhikariR. VermaG. KhandelwalI. 2015 A model ranking based selective ensemble approach for time series forecasting Procedia Computer Science 48 14 21 https://doi.org/10.1016/j.procs.2015.04.104 10.1016/j.procs.2015.04.104 Search in Google Scholar

Ahmed, F., Asif, R., Hina, S., & Muzammil, M. (2017). Financial market prediction using Google Trends. International Journal of Advanced Computer Science and Applications, 8(7), 388–391. https://doi.org/10.14569/IJACSA.2017.080752 AhmedF. AsifR. HinaS. MuzammilM. 2017 Financial market prediction using Google Trends International Journal of Advanced Computer Science and Applications 8 7 388 391 https://doi.org/10.14569/IJACSA.2017.080752 10.14569/IJACSA.2017.080752 Search in Google Scholar

Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3), 175–185. https://doi.org/10.2307/2685209 AltmanN. S. 1992 An introduction to kernel and nearest-neighbor nonparametric regression The American Statistician 46 3 175 185 https://doi.org/10.2307/2685209 10.2307/2685209 Search in Google Scholar

Beyaz, E., Tekiner, F., Zeng, X. J., & Keane, J. (2018). Stock Price Forecasting Incorporating Market State. 2018 IEEE 20th International Conference on High Performance Computing and Communications. https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00263 BeyazE. TekinerF. ZengX. J. KeaneJ. 2018 Stock Price Forecasting Incorporating Market State 2018 IEEE 20th International Conference on High Performance Computing and Communications https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00263 10.1109/HPCC/SmartCity/DSS.2018.00263 Search in Google Scholar

Bontempi, G., Taieb, S., & Borgne, Y. A. (2012). Machine learning strategies for time series forecasting. In European business intelligence summer school (pp. 62–77). Berlin, Germany: Springer. https://doi.org/10.1007/978-3-642-36318-4_3 BontempiG. TaiebS. BorgneY. A. 2012 Machine learning strategies for time series forecasting In European business intelligence summer school 62 77 Berlin, Germany Springer https://doi.org/10.1007/978-3-642-36318-4_3 10.1007/978-3-642-36318-4_3 Search in Google Scholar

Box, G., & Jenkins, G. (1970). Time Series Analysis: Forecasting and Control. San Francisco, CA: Holden-Day. BoxG. JenkinsG. 1970 Time Series Analysis: Forecasting and Control San Francisco, CA Holden-Day Search in Google Scholar

Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785 ChenT. GuestrinC. 2016 Xgboost: A scalable tree boosting system In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785 794 https://doi.org/10.1145/2939672.2939785 10.1145/2939672.2939785 Search in Google Scholar

Chen, K., Zhou, Y., & Dai, F. (2015). A LSTM-based method for stock returns prediction: A case study of China stock market. In 2015 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata.2015.7364089 ChenK. ZhouY. DaiF. 2015 A LSTM-based method for stock returns prediction: A case study of China stock market In 2015 IEEE International Conference on Big Data (Big Data) https://doi.org/10.1109/bigdata.2015.7364089 10.1109/BigData.2015.7364089 Search in Google Scholar

Chou, J. S., & Tran, D. S. (2018). Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders. Energy, 165, 709–726. https://doi.org/10.1016/j.energy.2018.09.144 ChouJ. S. TranD. S. 2018 Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders Energy 165 709 726 https://doi.org/10.1016/j.energy.2018.09.144 10.1016/j.energy.2018.09.144 Search in Google Scholar

Vapnik, V., Drucker, H., Burges, C. J., Kaufman, L., & Smola, A. (1997). Support vector regression machines. Advances in Neural Information Processing Systems, 9, 155–161. VapnikV. DruckerH. BurgesC. J. KaufmanL. SmolaA. 1997 Support vector regression machines Advances in Neural Information Processing Systems 9 155 161 Search in Google Scholar

Eassa, A. (2018). Why NVIDIA's Stock Crashed. Retrieved from https://finance.yahoo.com/news/why-nvidia-apos-stock-crashed-122400380.html EassaA. 2018 Why NVIDIA's Stock Crashed Retrieved from https://finance.yahoo.com/news/why-nvidia-apos-stock-crashed-122400380.html Search in Google Scholar

Emir, S., Dincer, H., & Timor, M. (2012). A stock selection model based on fundamental and technical analysis variables by using artificial neural networks and support vector machines. Review of Economics & Finance, 2(3), 106–122. EmirS. DincerH. TimorM. 2012 A stock selection model based on fundamental and technical analysis variables by using artificial neural networks and support vector machines Review of Economics & Finance 2 3 106 122 Search in Google Scholar

Hatta, A. J. (2012). The company fundamental factors and systematic risk in increasing stock price. Journal of Economics, Business and Accountancy, 15(2), 245–256. http://doi.org/10.14414/jebav.v15i2.78 HattaA. J. 2012 The company fundamental factors and systematic risk in increasing stock price Journal of Economics, Business and Accountancy 15 2 245 256 http://doi.org/10.14414/jebav.v15i2.78 10.14414/jebav.v15i2.78 Search in Google Scholar

Hill, J. B., & Motegi, K. (2019). Testing the white noise hypothesis of stock returns. Economic Modelling, 76, 231–242. https://doi.org/10.1016/j.econmod.2018.08.003 HillJ. B. MotegiK. 2019 Testing the white noise hypothesis of stock returns Economic Modelling 76 231 242 https://doi.org/10.1016/j.econmod.2018.08.003 10.1016/j.econmod.2018.08.003 Search in Google Scholar

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 HochreiterS. SchmidhuberJ. 1997 Long short-term memory Neural Computation 9 8 1735 1780 https://doi.org/10.1162/neco.1997.9.8.1735 10.1162/neco.1997.9.8.1735 Search in Google Scholar

Ke, G., Meng, Q., Finley, T., Wang, T., & Chen, W. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154. KeG. MengQ. FinleyT. WangT. ChenW. 2017 Lightgbm: A highly efficient gradient boosting decision tree Advances in Neural Information Processing Systems 30 3146 3154 Search in Google Scholar

Kozachenko, L. F., & Leonenko, N. N. (1987). Sample estimate of the entropy of a random vector. Problemy Peredachi Informatsii, 23(2), 9–16. KozachenkoL. F. LeonenkoN. N. 1987 Sample estimate of the entropy of a random vector Problemy Peredachi Informatsii 23 2 9 16 Search in Google Scholar

Laptev, N., Yosinski, J., Li, L. E., & Smyl, S. (2017). Time-series extreme event forecasting with neural networks at Uber. International Conference on Machine Learning, 34, 1–5. LaptevN. YosinskiJ. LiL. E. SmylS. 2017 Time-series extreme event forecasting with neural networks at Uber International Conference on Machine Learning 34 1 5 Search in Google Scholar

Lo, A. W. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. The Journal of Portfolio Management, 30(5), 15–29. https://doi.org/10.3905/jpm.2004.442611 LoA. W. 2004 The adaptive markets hypothesis: Market efficiency from an evolutionary perspective The Journal of Portfolio Management 30 5 15 29 https://doi.org/10.3905/jpm.2004.442611 10.3905/jpm.2004.442611 Search in Google Scholar

Mahmoud, A., & Sakr, S. (2012). The predictive power of fundamental analysis in terms of stock return and future profitability performance in Egyptian Stock Market: Empirical Study. International Research Journal of Finance & Economics, 92(1), 43–58. MahmoudA. SakrS. 2012 The predictive power of fundamental analysis in terms of stock return and future profitability performance in Egyptian Stock Market: Empirical Study International Research Journal of Finance & Economics 92 1 43 58 Search in Google Scholar

Milosevic, N. (2016). Equity forecast: Predicting long term stock price movement using machine learning. Journal of Economics Library, 3(2), 288–294. http://doi.org/10.1453/jel.v3i2.750 MilosevicN. 2016 Equity forecast: Predicting long term stock price movement using machine learning Journal of Economics Library 3 2 288 294 http://doi.org/10.1453/jel.v3i2.750 Search in Google Scholar

Muhammad, S., & Ali, G. (2018). The relationship between fundamental analysis and stock returns based on the panel data analysis; evidence from Karachi Stock exchange (KSE). Research Journal of Finance and Accounting, 9(3), 84–96. MuhammadS. AliG. 2018 The relationship between fundamental analysis and stock returns based on the panel data analysis; evidence from Karachi Stock exchange (KSE) Research Journal of Finance and Accounting 9 3 84 96 Search in Google Scholar

Nvidia Corporation. (2018). Nvidia Corporation Annual Review. Retrieved from https://s22.q4cdn.com/364334381/files/doc_financials/annual/2018/NVIDIA2018_AnnualReview-(new).pdf. Nvidia Corporation 2018 Nvidia Corporation Annual Review Retrieved from https://s22.q4cdn.com/364334381/files/doc_financials/annual/2018/NVIDIA2018_AnnualReview-(new).pdf. Search in Google Scholar

Pai, P. F., & Lin, C. S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33, 497–505. https://doi.org/10.1016/j.omega.2004.07.024 PaiP. F. LinC. S. 2005 A hybrid ARIMA and support vector machines model in stock price forecasting Omega 33 497 505 https://doi.org/10.1016/j.omega.2004.07.024 10.1016/j.omega.2004.07.024 Search in Google Scholar

Preis, T., Moat, H. S., & Stanley, E. H. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, 3(1684), 1–6. https://doi.org/10.1038/srep01684 PreisT. MoatH. S. StanleyE. H. 2013 Quantifying trading behavior in financial markets using Google Trends Scientific Reports 3 1684 1 6 https://doi.org/10.1038/srep01684 10.1038/srep01684 Search in Google Scholar

Rather, A. M., Agarwal, A., & Sastry, V. N. (2015). Recurrent neural network and a hybrid model for prediction of stock returns. Expert Systems with Applications, 42(6), 3234–3241. https://doi.org/10.1016/j.eswa.2014.12.003 RatherA. M. AgarwalA. SastryV. N. 2015 Recurrent neural network and a hybrid model for prediction of stock returns Expert Systems with Applications 42 6 3234 3241 https://doi.org/10.1016/j.eswa.2014.12.003 10.1016/j.eswa.2014.12.003 Search in Google Scholar

Shim, J. K., & Siegel, J. G. (2007). Handbook of Financial Analysis, Forecasting, and Modeling (p. 255). Chicago, USA: CCH. ShimJ. K. SiegelJ. G. 2007 Handbook of Financial Analysis, Forecasting, and Modeling 255 Chicago, USA CCH Search in Google Scholar

Stanković, J., Marković, J., & Stojanović, M. (2015). Investment strategy optimization using technical analysis and predictive modeling in emerging markets. Procedia Economics and Finance, 19, 51–62. https://doi.org/10.1016/S2212-5671(15)00007-6 StankovićJ. MarkovićJ. StojanovićM. 2015 Investment strategy optimization using technical analysis and predictive modeling in emerging markets Procedia Economics and Finance 19 51 62 https://doi.org/10.1016/S2212-5671(15)00007-6 10.1016/S2212-5671(15)00007-6 Search in Google Scholar

Whittle, P. (1951). Hypothesis Testing in Time Series Analysis. Uppsala, Sweden: Almqvist & Wiksells boktr. WhittleP. 1951 Hypothesis Testing in Time Series Analysis Uppsala, Sweden Almqvist & Wiksells boktr Search in Google Scholar

Yang, K., & Shahabi, C. (2005). On the stationarity of multivariate time series for correlation-based data analysis. In Proceedings of the Fifth IEEE International Conference on Data Mining, Houston. https://doi.org/10.1109/ICDM.2005.109 YangK. ShahabiC. 2005 On the stationarity of multivariate time series for correlation-based data analysis In Proceedings of the Fifth IEEE International Conference on Data Mining Houston https://doi.org/10.1109/ICDM.2005.109 10.1109/ICDM.2005.109 Search in Google Scholar

Zeytinoglu, E., Akarim, Y. D., & Celik, S. (2012). The impact of market-based ratios on stock returns: The evidence from insurance sector in Turkey. International Research Journal of Finance and Economics, 84, 41–48. ZeytinogluE. AkarimY. D. CelikS. 2012 The impact of market-based ratios on stock returns: The evidence from insurance sector in Turkey International Research Journal of Finance and Economics 84 41 48 Search in Google Scholar

Zheng, A., & Jin, J. (2017). Using AI to make predictions on stock MARKET. Stanford University. Retrieved from http://cs229.stanford.edu/proj2017/final-reports/5212256.pdf ZhengA. JinJ. 2017 Using AI to make predictions on stock MARKET Stanford University Retrieved from http://cs229.stanford.edu/proj2017/final-reports/5212256.pdf Search in Google Scholar

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